RePP-C: Runtime estimation of performance-power with workload consolidation in CMPs

Configuration of hardware knobs in multicore environments for meeting performance-power demands constitutes a desirable feature in modern data centers. At the same time, high energy efficiency (performance per watt) requires optimal thread-to-core assignment. In this paper, we present the runtime estimator (RePP-C) for performance-power, characterized by processor frequency states (P-states), a wide range of sleep intervals (Cl-states) and workload consolidation. We also present a schema for frequency and contention-aware thread-to-core assignment (FACTS) which considers various thread demands. The proposed solution (RePP-C) selects a given hardware configuration for each active core to ensure that the performance-power demands are satisfied while using the scheduling schema (FACTS) for mapping threads-to-cores. Our results show that FACTS improves over other state-of-the-art schedulers like Distributed Intensity Online (DIO) and native Linux scheduler by 8.25% and 37.56% in performance, with simultaneous improvement in energy efficiency by 6.2% and 14.17%, respectively. Moreover, we prove the usability of RePP-C by predicting performance and power for 7 different types of workloads and 10 different QoS targets. The results show an average error of 7.55% and 8.96% (with 95% confidence interval) when predicting energy and performance respectively.